Global study of Gender Inequality in Education Access

Abstract

In this article we aim to explore gender inequality in education access across the globe. Education is one of the key drivers in the development of society. Using data obtained from the World Bank, our team analyzed the relationship between education attainment, gender, and other economic factors such as Gross Domestic Product (GDP), the Gross National Income (GNI) index, population, and other variables. The data utilized is from the years 2010, 2011, 2012, 2013, and 2014. This project focuses on a larger global scale and then provides the interactivity for readers to dive into specific regions, countries, and demographics in order to identify trends and patterns. We hope that you find this dataset as interesting and expository as we did - from regions with the highest diparity in education enrollment based on gender to the linkage of other factors that may be contributing to this disparity encourages us to ask questions about the forces at play (economic, political, social) affecting women’s access to education and thus to a better life.

Introduction

Education is one of the most important factors in the development of society. Throughout all history, women have been marginalized in the education system. This has led to a significant gap in the education. The United Nations has recognized this issue and has emphasized the importance of education in the sustainable development of society. While this gender gap has been closing over the years, we will aim to explore the current state of this gap and what are the factors that may be contributing to its continued existence so that we can better understand how to address this issue.

To do this, we will not only take into account factors like global development or macroeconomics but we will try to dive deeper into gender roles in society. We will address six critical questions:

• How do literacy rates correlate with population growth rates across different regions and time periods?
• How does the allocation of resources to education influence literacy rates?
• In what ways do literacy and education contribute to economic growth and development?
• To what extent does gender equality manifest in educational access, participation, and outcomes?
• How do trends in education mirror trends in employment, particularly regarding gender?
• What specific obstacles hinder gender equality in educational access, retention, and achievement?

By answering the above, we will be able to find out the importance of education in the development of countries and population, the influence it has within the working industry, what role economics plays in education, and explore further issues in the current gender gap. Hopefully, this study will help us better understand this gap and how to address it in the future.

Regions

Before analyzing the world, it is important to understand its nature. Analyzing the whole world can be overwhelming at times, due to its complexity and number of countries in it. For that reason, we need to understand how to divide it. The world Bank does a great job dividing it into 7 different regions: South Asia, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, East Asia & Pacific, Sub-Saharan Africa, and North America. We can see this division here:

This is a map of the classification of regions provided by the World Bank. Each country is colored by the region they are in, the numbers are insicators for each region, and you can zoom in and out with the + and - bottons, as well as move around the map by clicking and dragging.

This map is very useful for categorizing countries into regions. This regions have been selected by the World Bank due to their location and other similarities.

Teasing out Enrollment by Region

To begin our analysis, we will be looking at different regions usually studied by the World Bank and represented above. While this graph may not have a great impact in an analysis, we can see how higher level of education bubbles are smaller, specially the Tertiary level:

To begin our analysis, we will be looking at different regions usually studied by global organizations like the World Bank. We will be focusing on the following regions: South Asia, Europe & Central Asia, Latin America & Caribbean, Middle East & North Africa, East Asia & Pacific, Sub-Saharan Africa, and North America. The objective is to visualize the differences in women’s enrollment at different education levels across the world at a global scale and identify any trends or patterns that may arise. This will also allow us to consider if some regions are worth exploring in more detail than others in our analysis.

This chart shows the Female Gross Enrollment Ratios by Region for the year 2014. These region delimitations have been chosen from what global organizations like the World Bank utilize for their studies. You can select and deselect regions to focus on different regions, each bubble inside the region represents an education level, and the values inside are the female gross enrollment ration for 2014. The sizes of the bubbles correlate to the values. If levels of education are not present in a region is due to the lack of information about that specific combination of education level and region.

From the chart above, not surprisingly, we can see that for all regions, the higher the education level, the lower the enrollment ratio. This is expected as the higher the education level, the more specialized and less accessible it becomes. However, we can see that there are some regions that have higher enrollment ratios than others. Specifically, the regions with the lowest enrollment, specifically South Asia and Sub-Saharan Africa, followed by Mid due East & North Africa, Latin America & Caribbean, and East Asia & Pacific. However, the scope of regions limits our ability to make a thoughtful comparison, as we do not know if data has not been collected by the World Bank in some countries.

This is important information to consider as we move forward with our analysis. One can think that it may be due to economic reasons, social and ideological reasons, or even political reasons. However, this problem has been present for decades and instead of taking assumptions to answer this question, we should identify the key reasons for this disparity and address it accordingly.

Let’s Start with the World

Primary Ed.

These plots show the Gross Enrollment Ratio per education level around the world. Only the values collected by the World Bank are being displayed. Countries that did not share information do not display any values. Note that because of the way data is collected, small discrepancies in the reported age of children may occasionally cause net enrollment rates to exceed 100 percent by the World Bank. However, this allows us to visualize high vs low enrollment and contrast regions of interest.

Secondary Ed.

These plots show the Gross Enrollment Ratio per education level around the world. Only the values collected by the World Bank are being displayed. Countries that did not share information do not display any values. Note that because of the way data is collected, small discrepancies in the reported age of children may occasionally cause net enrollment rates to exceed 100 percent by the World Bank. However, this allows us to visualize high vs low enrollment and contrast regions of interest.

Tertiary Ed.

These plots show the Gross Enrollment Ratio per education level around the world. Only the values collected by the World Bank are being displayed. Countries that did not share information do not display any values. Note that because of the way data is collected, small discrepancies in the reported age of children may occasionally cause net enrollment rates to exceed 100 percent by the World Bank. However, this allows us to visualize high vs low enrollment and contrast regions of interest.

Economic Influences on Female Access to Education

The World Bank assigns the world’s economies to four income groups, low income, lower-middle, upper-middle, and high income.

Income Category	FY 2022	FY 2023	FY 2024
Low Income	≤ 1,045	≤ 1,085	≤ 1,135
Lower-middle	1,046 - 4,095	1,086 - 4,255	1,136 - 4,465
Upper-middle	4,096 - 12,695	4,256 - 13,205	4,466 - 13,845
High Income	≥ 12,695	≥ 13,205	≥ 13,846

The classifications are updated each year on July 1 and are based on the GNI per capita of the previous year (2021). GNI measures are expressed in United States dollars (USD), and are determined using conversion factors derived according to the Atlas method (The Atlas method smooths exchange rate fluctuations using a three-year moving average, price-adjusted conversion factor. The USD estimate of GNI per capita is derived by applying the Atlas conversion factor to estimates measured in local currency units (LCU)).

Government Spending (Dollar per person)

d7 = require('d3@7') 
primaryData = d7.csv('/Users/sheebamoghal/Desktop/DSAN_5200/data_viz_final_project/report/primary_1.csv', function(d){
  return {
    country_name: d.country_name,
    year1: +d.year1,
    year2: +d.year2,
  }
})
primaryData

secondaryData = d7.csv('secondary_1.csv', function(d){
  return {
    country_name: d.country_name,
    year1: +d.year1,
    year2: +d.year2,
  }
})
secondaryData

tertiaryData = d7.csv('tertiary_1.csv', function(d){
  return {
    country_name: d.country_name,
    year1: +d.year1,
    year2: +d.year2,
  }
})
tertiaryData

viewof metric = Inputs.radio(new Map([["Absolute", "absolute"], ["Relative", "relative"]]), {value: "absolute", label: "Change"})

viewof selectedDataset = Inputs.select(['Primary', 'Secondary', 'Tertiary'], {label: 'Dataset: ', value: 'Primary'})

chart = async () => {
  const selected = await selectedDataset;
  let data;
  if (selected === 'Primary') {
    data = await primaryData;
  } else if (selected === 'Secondary') {
    data = await secondaryData;
  } else if (selected === 'Tertiary') {
    data = await tertiaryData;
  }
  
  data = data.map(d => ({
    country_name: d.country_name,
    value: metric === 'absolute' ? d.year2 - d.year1 : (d.year2 - d.year1) / d.year1
  }));

  const barHeight = 25;
  const marginTop = 50; 
  const marginRight = 70;
  const marginBottom = 50; 
  const marginLeft = 120;
  const height = Math.ceil((data.length + 0.1) * barHeight) + marginTop + marginBottom;

  const x = d3.scaleLinear()
    .domain(d3.extent(data, d => d.value))
    .rangeRound([marginLeft, width - marginRight]);

  const y = d3.scaleBand()
    .domain(data.map(d => d.country_name))
    .rangeRound([marginTop, height - marginBottom])
    .padding(0.1);

  const format = d3.format(metric === 'absolute' ? '+,d' : '+.1%');
  const tickFormat = metric === 'absolute' ? d3.formatPrefix('+.1', 1e1) : d3.format('+.0%');

  const svg = d3.create('svg')
    .attr('viewBox', [0, 0, width, height])
    .attr('style', 'max-width: 100%; height: auto; font-family: monospace; font-size: 10px;');

  // Chart Title
  svg.append('text')
    .attr('x', width / 2)
    .attr('y', 13)
    .attr('text-anchor', 'middle')
    .style('font-size', '16px')
    .style('font-weight', 'bold')
    .text('Government Spending for Education');

  // X-axis Label
  svg.append('text')
    .attr('x', width / 2)
    .attr('y', height - 15)
    .attr('text-anchor', 'middle')
    .style('font-size', '12px')
    .text('Dollar / Per Person');

  // Y-axis Label
  svg.append('text')
    .attr('transform', `rotate(-90)`)
    .attr('x', -(height / 2))
    .attr('y', marginLeft - 40)
    .attr('text-anchor', 'middle')
    .style('font-size', '12px')
    .text('Country');

  // Remaining SVG drawing code unchanged, add bars, texts, and axes
  svg.append('g')
    .selectAll('rect')
    .data(data)
    .join('rect')
    .attr('fill', d => d.value > 0 ? '#3d6469' : '#d40637')
    .attr('x', d => x(Math.min(d.value, 0)))
    .attr('y', d => y(d.country_name))
    .attr('width', d => Math.abs(x(d.value) - x(0)))
    .attr('height', y.bandwidth());

  svg.append('g')
    .attr('font-family', 'monospace')
    .attr('font-size', 10)
    .selectAll('text')
    .data(data)
    .join('text')
    .attr('text-anchor', d => d.value < 0 ? 'end' : 'start')
    .attr('x', d => x(d.value) + Math.sign(d.value) * 4)
    .attr('y', d => y(d.country_name) + y.bandwidth() / 2)
    .attr('dy', '0.35em')
    .text(d => format(d.value));

  svg.append('g')
    .attr('transform', `translate(0,${marginTop})`)
    .call(d3.axisTop(x).ticks(width / 80).tickFormat(tickFormat))
    .call(g => g.selectAll('.tick line').clone()
      .attr('y2', height - marginTop - marginBottom)
      .attr('stroke-opacity', 0.1))
    .call(g => g.select('.domain').remove());

  svg.append('g')
    .attr('transform', `translate(${x(0)},0)`)
    .call(d3.axisLeft(y).tickSize(0).tickPadding(6))
    .call(g => g.selectAll('.tick text').filter((d, i) => data[i].value < 0)
      .attr('text-anchor', 'start')
      .attr('x', 6));

  return svg.node();
}

chart();

Sources

https://blogs.worldbank.org/en/opendata/new-world-bank-country-classifications-income-level-2022-2023